d5ffba099e
Permanently integrates the Atomizer-Field GNN surrogate system: - neural_models/: Graph Neural Network for FEA field prediction - batch_parser.py: Parse training data from FEA exports - train.py: Neural network training pipeline - predict.py: Inference engine for fast predictions This enables 600x-2200x speedup over traditional FEA by replacing expensive simulations with millisecond neural network predictions. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
"""
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Script to investigate unit conversion issues in AtomizerField
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"""
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import json
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import h5py
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import numpy as np
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print("="*60)
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print("UNIT VALIDATION CHECK")
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print("="*60)
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# Load JSON metadata
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with open('test_case_beam/neural_field_data.json', 'r') as f:
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data = json.load(f)
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# Check units
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print("\n1. UNITS FROM METADATA:")
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print("-"*60)
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units = data['metadata']['units']
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for key, value in units.items():
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print(f" {key}: {value}")
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# Check loads
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print("\n2. APPLIED LOADS:")
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print("-"*60)
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loads = data['loads']
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print(f" Available load types: {list(loads.keys())}")
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if 'point_forces' in loads:
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point_forces = loads['point_forces']
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print(f" Point forces: {len(point_forces)} applied")
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if point_forces:
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print("\n Sample forces (first 3):")
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for i in range(min(3, len(point_forces))):
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force = point_forces[i]
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print(f" Force {i+1}:")
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print(f" Node: {force['node']}")
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print(f" Magnitude: {force['magnitude']:.2e} N")
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print(f" Direction: {force['direction']}")
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# Load HDF5 data
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print("\n3. REACTION FORCES FROM HDF5:")
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print("-"*60)
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with h5py.File('test_case_beam/neural_field_data.h5', 'r') as f:
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reactions = f['results/reactions'][:]
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# Get statistics
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non_zero_reactions = reactions[np.abs(reactions) > 1e-10]
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print(f" Shape: {reactions.shape}")
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print(f" Min: {np.min(reactions):.2e}")
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print(f" Max: {np.max(reactions):.2e}")
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print(f" Mean (non-zero): {np.mean(np.abs(non_zero_reactions)):.2e}")
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print(f" Max absolute: {np.max(np.abs(reactions)):.2e}")
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# Check displacement for comparison
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displacement = f['results/displacement'][:]
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max_disp = np.max(np.abs(displacement[:, :3])) # Translation only
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print(f"\n Max displacement: {max_disp:.6f} mm")
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# Check stress
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print("\n4. STRESS VALUES FROM HDF5:")
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print("-"*60)
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with h5py.File('test_case_beam/neural_field_data.h5', 'r') as f:
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stress = f['results/stress/cquad4_stress/data'][:]
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# Von Mises stress is in column 7 (0-indexed)
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von_mises = stress[:, 7]
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print(f" Shape: {stress.shape}")
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print(f" Von Mises min: {np.min(von_mises):.2e} MPa")
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print(f" Von Mises max: {np.max(von_mises):.2e} MPa")
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print(f" Von Mises mean: {np.mean(von_mises):.2e} MPa")
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# Check if values make sense
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print("\n5. SANITY CHECK:")
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print("-"*60)
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print(f" Units claimed: force=N, stress=MPa, length=mm")
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print(f" Max reaction force: {np.max(np.abs(reactions)):.2e} N")
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print(f" Max von Mises: {np.max(von_mises):.2e} MPa")
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print(f" Max displacement: {max_disp:.6f} mm")
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# Typical beam: if force is 1000 N, stress should be ~10-100 MPa
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# If reaction is 152 million N, that's 152,000 kN - VERY high!
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max_reaction = np.max(np.abs(reactions))
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max_stress_val = np.max(von_mises)
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print(f"\n If force unit is actually kN instead of N:")
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print(f" Max reaction: {max_reaction/1000:.2e} kN")
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print(f" If stress unit is actually Pa instead of MPa:")
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print(f" Max stress: {max_stress_val/1e6:.2e} MPa")
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print("\n6. HYPOTHESIS:")
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print("-"*60)
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if max_reaction > 1e6:
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print(" [!] Reaction forces seem TOO LARGE (>1 MN)")
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print(" [!] Possible issue: pyNastran returns forces in different units")
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print(" [!] Check: Nastran may export in base units (N) while expecting kN")
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if max_stress_val > 1e6:
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print(" [!] Stresses seem TOO LARGE (>1000 MPa)")
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print(" [!] Possible issue: pyNastran returns stress in Pa, not MPa")
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print("\n" + "="*60)
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